1. Field of the Invention
The present invention relates to imaging systems. More specifically, the present invention relates to systems for mapping earth based resources using infrared radiometric sensors.
2. Description of the Related Art
Earth resource management is critical for optimal agricultural and other applications. In agricultural applications, earth resource management involves, by way of example, the timed application of controlled amounts of water, fertilizer, pesticides and other elements. In this area, both subjective knowledge and objective data are now amassed in computer-based programs allowing human interaction and providing machine interpretation. These new mechanisms represent a vast improvement over past methods for processing data. The major benefit to agriculture has been the objective integration of knowledge into traditional agricultural practices. Decision support services (DSS) and Expert Systems (ES) have been developed to focus resources in areas of most need where payoff potential is greatest. Included within this range of capabilities are such areas as: nutrient management, insecticide management, crop growth management, soil erosion management, resource management and irrigation management.
There is an ongoing need in the art for optimal management in each of these areas. This requires manual or automatic sensing of one or more indicators. For such applications, thermal sensing techniques are very useful inasmuch as, with respect to crop temperatures, thermal sensing provides an indication of the temperature of the plant which provides an early indication of numerous parameters including proper irrigation level, the presence of parasites and on. Conventional sensing approaches include: 1) soil moisture measurement techniques, 2) plant sampling techniques, 3) atmospheric measurement techniques, and 4) remote sensing techniques.
There are numerous soil moisture measurement techniques including: gravametric sampling, soil tension, soil salinity content, neutron moisture meters, time domain refractometers, and etc. In addition, soil and plant may be sampled manually by feel.
There are numerous experimental and theoretical studies addressing the use of surface temperature and reflectance to gain information about a variety of plant and soil properties. Crop and soil reflectance has been related to green biomass and leaf area index, crop phenology, absorbed photosynthetically active radiation, plant carbon storage and light use efficiency and long term fluxes of nutrients and carbon between various components of the ecosystem. Research using high resolution spectrometers has focused attention on several fertile areas for potential improvements in our ability to detect plant response to stress. One such phenomenon is a shift in the "red edge" of plant reflectance spectra. When plants are stressed, there is a change in concentration of chlorophyll pigments and the red edge moves towards shorter wavelengths (Gates et al., 1965; Horler et al., 1983). This is most noticeable in an examination of the first derivative of spectral response (Demetriades-Shah and Steven, 1988; Demetriades-Shah et al., 1990). There is some expectation that red shift behavior will be independent of the amount of background soil viewed by the radiometer, thus removing a significant obstacle to the interpretation of remotely sensed imagery (Schutt et al., 1984).
Surface temperature has been related to soil moisture content (Jackson et al., 1977b; Jackson, 1982), plant water stress (Jackson et al., 1977a, Idso et al., 1978; Idso, 1982 and Jackson and Pinter, 1981), and plant transpiration rate (Idso et al., 1977b; Jackson et al., 1983). The Idso-Jackson crop water stress index (CWSI), derived from measurements of foliage temperature (Idso et al., 1981; Jackson et al., 1981), has been shown to be closely correlated with soil moisture content, soil water matrix potential, soil salinity, soil waterlogging, plant water potential, leaf diffusion resistance and photosynthesis, as well as final crop yield (see historical reviews by Jackson, 1987 and Idso et al., 1986). These research results led to the use of CWSI for such important farm applications as irrigation scheduling, predicting crop yields and detecting certain plant diseases (Jackson et al., 1977a; Idso et al., 1977a, Reginato et al., 1978; Jackson et al., 1980, Pinter et al., 1979).
Combining surface reflectance and temperature with meteorological data, methods have been developed to estimate evaporation (ET) rates over large areas (Carison et al., 1981; Gurney and Hall, 1983; Price, 1980, 1982, 1990; Running et al., 1989; Soer, 1980; Taconet et al., 1986; Jackson, 1985; Moran and Jackson, 1991). This technique has been successfully applied to mature agricultural fields using ground-, aircraft- and space-based sensors (Reginato et al., 1985; Jackson et al., 1987; Moran et al.,1990b; Moran et al., 1994a) and, with some refinements, to an arid rangeland site (Kustas et al.,1989; Moran et al., 1994c). A method for incorporating remotely sensed spectral and thermal data into simulation models of crop growth and ET has been described (Maas et al., 1985, 1989). Applied to a region, such models, based on infrequent remotely sensed observations, may provide a continuous description of ET over time (Maas et al., 1992; Moran et al., 1992b).
Unfortunately, soil moisture measurement systems are often require wires, tubing and/or special expertise for setup, calibration, operation and maintenance. Plant sampling is often destructive to the crop. Hence, these systems tend to be labor intensive and expensive. More importantly, plant sampling and soil moisture measurement techniques are point source measurement techniques. These systems provide a specific plant or, at best, conditions in the region of a sample. Accordingly, the accuracy of these systems is limited to the extent to which a region is adequately sampled. On the other hand, as the number of samples are increased, the cost increases accordingly.
Atmospheric measurements techniques involve a calculation of water demand based on regional atmospheric conditions. However, this technique is indirect and therefore somewhat inaccurate. The required weather stations are expensive to setup and maintain and the sampling is somewhat localized as is the case with soil moisture measurement and plant sampling techniques discussed above.
At least three thermal remote sensing techniques are known in the art: satellite based systems, aircraft based systems and hand held systems.
The use of satellite data for evaluating temporal changes in surface conditions requires that the data be corrected for atmospheric influences. For visible and near-infrared (IR) wavelengths, this can be accomplished by measuring atmospheric optical depth and using a radiative transfer code to compute the relationship between surface reflectance and radiance at the sensor (Holm et al., 1989; Moran et al., 1990a). However, this procedure is too expensive and time consuming to be used on an operational basis. Other atmospheric correction procedures have been proposed (Otterman and Fraser, 1976; Singh, 1988; Dozier and Frew, 1981; Teillet, 1986), but few have been validated with ground data under different atmospheric conditions and most are dependent upon the use of a radiative transfer model. Ahern et al. (1977) proposed an image-based method for atmospheric correction that eliminated the need for on-site measurements of atmospheric conditions, termed the dark-object subtraction method. Moran et al. (1992a) examined several correction procedures, including four radiative transfer codes (RTC), dark-object subtraction (DOS) and a modified DOS approach, to determine which technique could provide both ease and accuracy. A modified DOS approach, which combined the image-based nature of DOS with the precision of the RTC, provided sufficient accuracy and simplicity to warrant further development.
Image data acquired by satellite and aircraft sensors are usually view angle dependent as a result of a combination of atmospheric effects and the bi-directional surface reflectance of non-lambertian targets (Holben and Fraser, 1984; Staenz et al., 1981). The view-angle effect due to the non-lambertian character of natural surfaces can be removed if the bi-directional reflectance distribution function (BRDF) of each target type is known and a suitable correction algorithm is implemented. Most research has focused on empirical interpretation of bi-directional measurements made using airborne radiometers (Salmonson and Marlatt, 1968, 1971), for vegetation components (Breece and Holmes, 1971; Moran et al., 1989) and canopies (Pinter et al., 1985, 1990; Deering, 1988; Kimes et al., 1985; Shibayama and Wiegrand, 1985; Kriebel, 1978), as well as bare soil and other non-vegetated surfaces (Jackson et al., 1990; Doering et al., 1989; Deering and Leone, 1986; Walthall et al., 1985; Becker et al., 1985; Kimeset al., 1984; Kimes, 1983a). More recently, extensive modeling studies have also been undertaken (e.g., Pinty et al., 1989; Goel and Reynolds, 1989; Ross and Marshak, 1988; Gerstl, 1988; Cierniewski, 1987; Gerstl and Simmer, 1986; Kimes et al., 1986; Kirchner et al., 1981; Egbert, 1977). Using these canopy BRDF models, it has been possible to develop operational methods for normalizing differences in spectral response (Cabot et al., 1994; Qi et al., 1994) and to investigate bi-directional measurements as a source of information about crop stress and structure (Cabot et al., 1994; Qi et al., 1994). Qi et al. (in press) found that by combining outputs from several BRDF models, they could retrieve such crop and plant parameters as leaf area index (LAI), leaf reflectance (p) and leaf transmittance (r). Unlike a simple vegetation index (such as NDVI), this information provides the farmers with meaningful measures of crop growth and stress. Knowledge of LAI, p and r are not only useful as direct measures of crop status but are the basic inputs to crop growth and yield models.
Few concerted efforts to use remotely sensed information in crop growth models have been made. In an early attempt by Arkin et al. (1977), it was recognized that values of model variables could be updated with observed values based on spectral measurements. They proposed the concept of a hybrid "spectral-physiological" model capable of using satellite spectral data. Since then, spectral estimates of leaf area index (LAI), soil moisture, plant green biomass, plant stress and surface evapotranspiration have been incorporated into plant growth and yield models for wheat, cotton, corn, soybeans, sunflower, sorghum, and rangeland vegetation (Hodges and Kanemasu, 1977; Kanemasu et al., 1985; Wanjura and Hatfield, 1985; Asrar et al., 1985; Maas et al., 1985, 1989, 1992; Delecolle and Guerif, 1988; Maas 1988a, 1988b, 1992). Maas (1993a; 1993b) described a method for within-season simulation calibration based on remotely-sensed observations of crop growth obtained during the growing season. This work offers a simple approach to modeling crop growth and yield which requires less ground information by supplementing the model with periodic estimates of key input parameters using remote sensing.
In any event, there are numerous shortcomings associated with conventional remote sensing techniques. Hand held units require considerable expertise, are labor intensive and generate highly localized samples.
Satellite based remote sensing systems have several shortcomings. The data is not delivered in a timely basis. Satellite sensing requires expensive, labor intensive work stations to download the data. The reliability of the data is dependent on atmospheric conditions at the time the area is scanned in that cloud cover can severely attenuate the sensed signals. Further, the resolution of the system is typically fixed and not easily changed. Remote sensing systems used in agriculture do not have the spatial resolution to distinguish plants from soil. This is especially true concerning row crops. Therefore, the need exists to develop and perform extensive modeling to correct the deficiency as required. Additionally, satellites cannot physically sample the ambient air in the atmosphere, further adding to the dynamic modeling requirements and complexity. Finally, the images created are not typically georeferenced nor corrected for tilt. Failure to accurately reference the images to the ground and to correct for a tilt in the sensor make it difficult if not impossible to accurately combine images from individual scans to create a composite map or mosaic generally considered to be useful for agricultural, fire fighting and other earth based resource management applications.
Conventional aircraft based infrared imaging systems have a limited mapping capability. These systems typically look at a single square mile area at one time. The images taken are not georeferenced or corrected for tilt. Accordingly, conventional aircraft based infrared imaging systems do not generate large accurate maps at a high level of resolution on a repeatable basis. This affects the flight height of the aircraft in that the craft must fly at a height at which it can acquire the target area within the field of view of the sensor. Unfortunately, the height at which this can be achieved typically forces a compromise in the resolution of the detected image. Inadequate image resolution limits the extent to which the crop may be distinguished from its background. This, in turn, limits the extent to which crop water stress management may be performed. Finally, the inability to mosaic images delays the delivery of data due to the time required for image processing and assembly; and delivery to the client on a timely basis (within 24 to 36 hours) is usually not possible on a repeatable basis.
Hence, there is a need in the art for an accurate and inexpensive system and technique for IR mapping of large areas at high resolution on a repeatable basis in a timely manner.